For all the gradient-learning-based models, we initialized the parameters from a normal distribution \(\mathcal {N}(0, 0.01)\)  and trained them using an Adam optimizer [1]}. By default, the batch size was 512. We conducted the grid search over the hyperparameters of all the models, and tuned the learning rate among {0.1, 0.01, 0.001} and the \(L_2\) -regularization term among {0.001, 0.01, 0.1}. As an early stopping condition, we used 50 epochs of patience.
